李俊,杜钧,刘羽,许建玉. 2017. 不同扰动方法集合离散度演变的异同性暨地形扰动初探[J]. 气象学报, 75(1):123-146, doi:10.11676/qxxb2017.011
不同扰动方法集合离散度演变的异同性暨地形扰动初探
Similarities and differences in the evolution of ensemble spread using various ensemble perturbation methods including topography perturbation
投稿时间:2016-01-19  最后修改时间:2016-10-28
DOI:10.11676/qxxb2017.011
中文关键词:  北京"7.21"大暴雨  集合预报  地形扰动  初值和物理扰动  离散度的异同性
英文关键词:Beijing "7.21" heavy rain  Ensemble prediction  Topography perturbation  Initial condition and physics perturbation  Similarity and difference in ensemble spread
基金项目:国家自然科学基金项目(41275107);公益性行业(水利)科研专项(201201063)。
作者单位
李俊 中国气象局武汉暴雨研究所, 武汉, 430074 
杜钧 美国国家海洋大气局国家环境预报中心, 华盛顿, 美国 
刘羽 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室, 北京, 100101;中国科学院大学, 北京, 100049 
许建玉 中国气象局武汉暴雨研究所, 武汉, 430074 
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中文摘要:
      基于北京“7.21”特大暴雨个例,设计了一种考虑地形不确定性对降水影响的集合预报方案,在对该方案进行初步评估的基础上,重点通过计算相关系数、扰动总能量和尺度分解,对包括地形扰动方案在内的4种集合预报方案(初值、多物理、地形、初值-多物理混合)中离散度演变的异同性进行了分析。结果表明:(1)考虑模式地形不确定性的扰动方案,在不影响集合平均降水预报质量的基础上,对集合降水预报的离散度和概率预报略有正贡献。(2)离散度空间结构的演变与天气形势的演变密切相关。不同扰动方案产生的离散度在初始时刻的空间分布各不相同,但随模式向前积分其离散度的相似度快速增大,其中0-6 h内增长速度最快,离散度场之间的相关系数可以超过0.6。混合方案与单一扰动方案相比,对离散度空间结构的贡献不大。(3)虽然不同方案的离散度空间结构相似,但其幅度却存在明显的差异,如地形扰动方案的离散度幅度明显小于初值扰动和物理过程扰动方案。混合方案可以增加原单一扰动方案的离散度振幅,但这种增加在高层明显,而在近地面层并不明显,因而增加降水和其他近地面大气变量的离散度要比增加上层大气变量的离散度更困难。(4)尺度分离的结果表明,随着空间尺度的增大和积分时间的延长,不同扰动方法产生的离散度结构会逐渐变得相似,但在积分早期(<12 h)和较小的空间尺度(<448 km)上离散度结构的差异仍明显,并且在较小的空间尺度(<448 km)上,不同扰动方法产生的离散度幅度有明显的差异。所以对于小空间尺度系统或甚短期预报,选择扰动方案比大尺度和较长期的预报更重要。以上研究可为集合预报如何合理采用不同扰动方案或不同方案的组合提供科学依据。
英文摘要:
      A terrain perturbation scheme (ter) has been first incorporated into an ensemble prediction system (EPS) and preliminarily tested in the simulation of the extremely heavy rain event occurred on 21 July, 2012 in Beijing. Along with other three perturbation schemes (i.e., initial condition (ic), multi-physics (phy), and one mixed forms (icphy)), similarities and differences in ensemble spread among these schemes have been compared mainly in terms of spatial correlation, total perturbation energy, and scale decomposition. The results show that:(1) the ensemble spread and probabilistic forecasts have been slightly improved by incorporating terrain uncertainty while the ensemble mean of precipitation forecasts remains similar in quality; (2) the spatial structure evolution of the ensemble spread is closely related to the weather system. Quite different initial patterns rapidly evolve into a similar pattern (the correlation coefficient reaches 0.6 or above) in the first 6 hours of model integration for all the schemes. Therefore, little extra information was gained about the spread structure by the mixed icphy scheme compared to either the ic or the phy single-source scheme; (3) differences in the ensemble spread magnitude are, however, large among these schemes, i.e. the magnitude of phy is the largest, that of ter is the smallest, and the magnitude of ic is in between. The mixture of ic and phy perturbations can noticeably boost the spread magnitude for upper level variables but not for near-surface variables, which implies that it is more challenging to increase ensemble spread for near-surface variables including precipitation in an EPS; (4) the scale decomposition of ensemble spread shows that the similarity of ensemble spread structure among the schemes generally increases with increases in spatial scale and forecast hours, but the differences remain large over small scales (<448 km) and for very short forecast range (<12 h); over small scales the differences in the spread magnitude are also obvious among the schemes. Therefore, the selection of perturbation methods in an EPS is more important for small scale and very short-range forecasts than for large scales and long-range forecasts. This study can be used as a guideline to design an effective EPS.
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